glm-model evaluation
I'd add that showing predictive ability is very important if the goal of the modeling process is to make predictions (and even if it's not, showing predictive ability provides support for the model). Frank Harrell has tools in the Design library for efficient internal validation and calibration via the bootstrap (see the 'validate' and 'calibrate' functions) but these will not work on a model produced by glm.nb. However it's easy to code a cross-validation in R and I believe MASS shows a 10-fold cross-validation for the CPUs example.
IIRC, there's a section in B&A (2002) that points out and demonstrates that AIC model selection has the property of being equivalent to "leave one out" cross-validation. It draws from an original work by Stone (197x ??). They also discuss more involved simulation-based (bootstrap) methods for complex models. ----- David Hewitt Research Fishery Biologist USGS Klamath Falls Field Station (USA)
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